Identification of transcription factor binding sites from the upstream regions of genes is a highly important and unsolved problem. In this paper, we propose a novel framework for using evolutionary algorithm to solve...
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ISBN:
(纸本)0769525288
Identification of transcription factor binding sites from the upstream regions of genes is a highly important and unsolved problem. In this paper, we propose a novel framework for using evolutionary algorithm to solve this challenging issue. Under this framework, we use two prevalent evolutionary algorithms: Genetic, Algorithm (GA) and Particle Swarm Optimization (PSO) to find unknown sites in a collection of relatively long intergenic sequences that are suspected of being bound by the same factor. This paper represents binding sites motif to position weight matrix (PWM) and introduces how to code PWM to genome for GA and how to code it to particle for PSO. We apply these two algorithms to 5 different yeast Saccharomyces Cerevisiae transcription factor binding sites and CRP binding sites. The results on Saccharomyces Cerevisiae show that it can find the correct binding sites motifs, and the result on CRP shows that these two algorithms can achieve more accuracy than MEME and Gibbs Sampler.
Newton's algorithm for constructing univariate interpolation polynomial is well-known. In this paper, we generalize Newton's formula to multivariate Lagrange interpolation. For some type of subset ΧL, so-call...
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Newton's algorithm for constructing univariate interpolation polynomial is well-known. In this paper, we generalize Newton's formula to multivariate Lagrange interpolation. For some type of subset ΧL, so-called lower subset, of a tensor product grid, we present directly an interpolation basis of Newton type which spans a minimal degree interpolation space for Lagrange interpolation on ΧL. We show that this basis is always a Newton interpolation basis for arbitrary ordered set of point evaluation functionals.
To improve artificial intelligence (AI) of computer games is a hard problem. Qualitative spatial reasoning is utilized to solve this problem. In qualitative spatial reasoning various aspects of qualitative spatial rea...
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To improve artificial intelligence (AI) of computer games is a hard problem. Qualitative spatial reasoning is utilized to solve this problem. In qualitative spatial reasoning various aspects of qualitative spatial reasoning such as topology, direction, size and distance have been widely investigated in pervious literatures. Although, the combination works of two or more spatial aspects are more useful than the single one in computer game and other applications, most combining problems have not been discussed before. So a unified model for qualitative topology and distance information is proposed. Finally this method is applied to strategy computer games.
In this paper a new technique for computing and ray tracing point based geometry is presented. It uses a novel point primitive that is called "Spherical Patch Point" (SPP) to approximate the vicinity of a su...
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In this paper a new technique for computing and ray tracing point based geometry is presented. It uses a novel point primitive that is called "Spherical Patch Point" (SPP) to approximate the vicinity of a surface point. Due to property of curvature, SPP can achieve similar visual quality compared with previous methods with much fewer primitives for the cost of a few additional bytes per point and thus makes a significant reduction in rendering time. During pre-process,important attributes are added to each SPP for the purpose of ray tracing. During rendering, an intersection algorithm different from previous ones has been demonstrated to get satisfied results. The proposed technique makes it possible to render high quality ray traced images with global illumination using SPPs. It offers a higher ray tracing speed in comparison with previous methods.
In this paper a new point-based rendering method for ray tracing is presented. An oriented spherical patch that passes a surface point is used to approximate the vicinity of that *** this paper the spherical patch tog...
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In this paper a new point-based rendering method for ray tracing is presented. An oriented spherical patch that passes a surface point is used to approximate the vicinity of that *** this paper the spherical patch together with the surface point is called "Spherical Patch Point" (SPP). Due to property of curvature, SPP can achieve similar visual quality compared with previous methods with much fewer points. This paper defines new point attributes for the purpose of efficiently locating the intersection between incoming ray and ***, an algorithm of intersecting a ray with point geometry is proposed. The algorithm can achieve a higher rendering speed in comparison with previous methods. The presented technique deals well with shadow, reflection and refraction.
Large scale terrain visualization with high-resolution has an increasing demand in many research fields. To realize the efficient rendering of terrain, this paper presents an out-of-core terrain visualization method b...
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A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN ...
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A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN is called dynamic growing because it is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule. When DGNN performs unsupervised learning, post-prune is carried out to prevent over fitting the training data just like decision tree learning. DGNN's prune rule is based on the distance threshold. DGNN has some advantages: learning not only is stable because it grows under certain conditions; but also it is faster than back-propagation rules and favorable learned predictive accuracy in small, noisy, online or offline data sets. Three classes of simulations are performed on the primary benchmarks: circle-in-the-square and two-spirals-apart benchmarks are used to check DGNN's supervised learning and compare it with ARTMAP and BP neural networks; DGNN's unsupervised learning ability is checked on UCI Machine Learning Archive's Synthetic Control Chart Time Series data set
In recently years there has been plenty of interest in Random Constraint Satisfaction Problem, both from an experimental and a theoretical point of view. In this paper we study and analyze the four popular problem ins...
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In recently years there has been plenty of interest in Random Constraint Satisfaction Problem, both from an experimental and a theoretical point of view. In this paper we study and analyze the four popular problem instance generating models, and present the extended model B+ based on the most used model B, which has the different domains and constraint tightness meeting some probability distribution function. In the subsequent section we give the relation matrix version of backtracking integrated forward checking algorithms, and introduce the implementation of instances generator and solver based on the new model. Finally we show the experiment results and conclude the paper, point that our extended model B+ has the common phase transition region with the transitional models and it has the advantage of being suited to the testing of heuristic based constraint solving algorithms, such as variables selection heuristic algorithms.
In Containing Order Rough Set Methodology (CORS),terminologies on rules or rules set, such as robust, minimality,completeness, mutuality degree, and conflict are discussed. The rules generation algorithm IGRs is given...
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In Containing Order Rough Set Methodology (CORS),terminologies on rules or rules set, such as robust, minimality,completeness, mutuality degree, and conflict are discussed. The rules generation algorithm IGRs is given and the details of algorithm IGRs are studied. Heuristic knowledge, which is mutuality degree of a condition item with a decision part, is used to choose condition item when generating rules. In primary and modified IGRs, two kinds of mutuality degree,simple and weighted mutuality are introduced respectively. In addition, the variable precision method is used to solve the conflict problem in modified IGRs. By experiments, the effects of two kinds heuristic knowledge and different weight values in synthetic mutuality on algorithms properties are shown,such as time consumption, calculation precision etc. The performances of IGRs with the primary and new conflict solution are compared by experiments.
The conclusion is that the weighted mutuality degree is more sound and the choice of appropriate weight values in it are important to optimize the quality of rules set. The variable precision method for dealing with conflict when generating rules is more reasonable. Both two modifications to primary IGRs make the performance of IGRs enhanced and the quality of rules set better. Algorithm IGRs still need further improvement.
A novel self Adaptive Support Vector Clustering algorithm (ASVC) is proposed in this paper to cluster dataset with diverse dispersions. And a Kernel function is defined to measure affinity between multi-relational dat...
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